MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202621071137 A) filed by Prof. Dr. Digambar Narsingrao Ganjewar on June 08, 2026, for System And Method For Real-Time Context-Aware Professional English Communication Enhancement Using Adaptive Language Processing And Intelligent Feedback Mechanisms.
Inventor includes Prof. Dr. Digambar Narsingrao Ganjewar.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: 069] The present invention discloses a system and method for real-time context-aware professional English communication enhancement using adaptive language processing, artificial intelligence, and machine learning techniques. The system comprises a communication input acquisition module, a context identification engine, a natural language understanding module, a professional communication assessment engine, a recommendation generation module, a personalized learning engine, an intelligent feedback delivery interface, and a machine learning optimization engine configured to collaboratively analyze communication content and provide adaptive communication enhancement recommendations in real time. The invention receives textual, voice-based, or multimodal communication inputs from multiple communication platforms and determines contextual parameters including communication objectives, audience characteristics, professional domains, and interaction environments. Advanced linguistic, semantic, syntactic, sentiment, and intent analyses are performed to evaluate communication quality, professionalism, clarity, tone consistency, and contextual appropriateness. Based on the analysis results, the system generates intelligent recommendations including grammar corrections, vocabulary enhancements, sentence restructuring suggestions, tone optimization guidance, and audience-specific communication adaptations. The personalized learning engine continuously updates user communication profiles and adapts recommendations according to individual proficiency levels and interaction history, while the machine learning optimization engine refines system performance using communication outcomes and feedback data. The invention provides improved communication effectiveness, enhanced professional language quality, personalized communication development, and continuous learning capabilities across diverse professional communication environments. Accompanied Drawing [FIGS. 1-2]
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